Sex by Educational Attainment

SEX BY AGE BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 18 YEARS AND OVER
Table: B15001 - Variables used: 4-42, 45-83.

#Loading the required packages
library(tidycensus)
library(tidyverse)
library(ggbeeswarm)
library(ggridges)
library(viridis)
library(hrbrthemes)
library(leaflet)
library(sf)
library(packcircles)
library(tigris)
options(tigris_use_cache = TRUE)
library(ggiraph)
library(shiny)

From the ACS 2019 5-year survey, we looked specifically at the educational attainment of MI residents 18 years and above. Our variables were already segmented by binary gender.

Below you will find Geographical visualizations that provide information on the following variables:

-male 18 to 24 with a High School diploma
-male 18 to 24 without a diploma
-female 18 to 24 with a High School diploma
-female 18 to 24 without a diploma
-male 25 to 34 with a High School diploma
-male 25 to 34 without a diploma
-female 25 to 34 with a High School diploma
-female 25 to 34 without a diploma
-male 35 to 44 with a High School diploma
-male 35 to 44 without a diploma
-female 35 to 44 with a High School diploma
-female 35 to 44 without a diploma
-male 45 to 64 with a High School diploma
-male 45 to 64 without a diploma
-female 45 to 64 with a High School diploma
-female 45 to 64 without a diploma
-male Above 65 with a High School diploma
-male Above 65 without a diploma
-female Above 65 with a High School diploma
-female Above 65 without a diploma

In the plot below, each point represents a county in Michigan. The plot shows the distributions of the percentage of people represented by each variable per county in Michigan.

We decided to create box plots that would further allow us to investigate the noticeable spread on all variables representing people with high school diplomas as shown below.

#selecting most interesting agevariables
cool_age_vars <- MI_age_percent %>%
  group_by(county) %>%
  filter(row_number() %in% c(2,4,6,8,10,12,14,16,18,20))

#box plot
ggplot(cool_age_vars, aes(x = variables, y = percent)) +
  geom_boxplot(
    
    # custom boxes
    color="blue",
    fill="blue",
    alpha=0.5,
    
    # Notch?
    notch=TRUE,
    notchwidth = 0.8,
    
    # custom outliers
    outlier.colour="red",
    outlier.fill="red",
    outlier.size=3
  ) +
  labs(title = "Individuals with a High School Diploma",
       caption = "Data Source: 2015-2019 ACS\n") +
  theme(plot.title = element_text(hjust = 0.5, size =12)) +
  coord_flip()

We noticed that the more females than males between 45 and 64 years had high school diplomas as seen from the means in the plot above although the difference is barely noticeable.

Interestingly, there are significantly more females than males above 65 years who have high school diplomas in Michigan.

To investigate this further, we decided to create interactive maps to show how the values of these four variables are distributed in all Michigan counties.

The map below shows the count of all males ages 45 to 65 without High School Diplomas in each county.

The map below shows the count of all females ages 45 to 65 without High School Diplomas in each county.

The map below shows the count of all males above 65 with a High School Diplomas in each county.

The map below shows the count of all females above 65 with a High School Diplomas in each county.

In the above maps, we see that the area around Detroit has the largest numbers while the counties in the upper peninsula have the least.